


import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil
@pytest.fixture
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		Any:
			UpperCamelCase__       =							tmp_path / "file.csv"
			UpperCamelCase__       =							textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20\n        "	)
			with open(_A					,  "w"	) as f:
						f.write(_A	)
			return str(_A	)
@pytest.fixture
def 							UpperCamelCase_(					_A :str	)->		str:
			UpperCamelCase__       =							tmp_path / "malformed_file.csv"
			UpperCamelCase__       =							textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20,\n        "	)
			with open(_A					,  "w"	) as f:
						f.write(_A	)
			return str(_A	)
@pytest.fixture
def 							UpperCamelCase_(					_A :List[str]					,  _A :Dict	)->		List[Any]:
			UpperCamelCase__       =							tmp_path / "csv_with_image.csv"
			UpperCamelCase__       =							textwrap.dedent(
			    F'''\
        image
        {image_file}
        '''	)
			with open(_A					,  "w"	) as f:
						f.write(_A	)
			return str(_A	)
@pytest.fixture
def 							UpperCamelCase_(					_A :Tuple	)->		Union[str, Any]:
			UpperCamelCase__       =							tmp_path / "csv_with_label.csv"
			UpperCamelCase__       =							textwrap.dedent(
			    "\\n        label\n        good\n        bad\n        good\n        "	)
			with open(_A					,  "w"	) as f:
						f.write(_A	)
			return str(_A	)
@pytest.fixture
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		str:
			UpperCamelCase__       =							tmp_path / "csv_with_int_list.csv"
			UpperCamelCase__       =							textwrap.dedent(
			    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "	)
			with open(_A					,  "w"	) as f:
						f.write(_A	)
			return str(_A	)
def 							UpperCamelCase_(					_A :Optional[int]					,  _A :Optional[int]					,  _A :Tuple	)->		List[Any]:
			UpperCamelCase__       =							Csv()
			UpperCamelCase__       =							csv._generate_tables([[csv_file, malformed_csv_file]]	)
			with pytest.raises(_A					,  match="Error tokenizing data"	):
						for _ in generator:
									pass
			assert any(
			    record.levelname == "ERROR"
			    and "Failed to read file" in record.message
			    and os.path.basename(_A	) in record.message
			    for record in caplog.records	)
@require_pil
def 							UpperCamelCase_(					_A :List[Any]	)->		Any:
			with open(_A					,  encoding="utf-8"	) as f:
						UpperCamelCase__       =							f.read().splitlines()[1]
			UpperCamelCase__       =							Csv(encoding="utf-8"					,  features=Features({"image": Image()}	)	)
			UpperCamelCase__       =							csv._generate_tables([[csv_file_with_image]]	)
			UpperCamelCase__       =							pa.concat_tables([table for _, table in generator]	)
			assert pa_table.schema.field("image"	).type == Image()()
			UpperCamelCase__       =							pa_table.to_pydict()["image"]
			assert generated_content == [{"path": image_file, "bytes": None}]
def 							UpperCamelCase_(					_A :Any	)->		Optional[int]:
			with open(_A					,  encoding="utf-8"	) as f:
						UpperCamelCase__       =							f.read().splitlines()[1:]
			UpperCamelCase__       =							Csv(encoding="utf-8"					,  features=Features({"label": ClassLabel(names=["good", "bad"]	)}	)	)
			UpperCamelCase__       =							csv._generate_tables([[csv_file_with_label]]	)
			UpperCamelCase__       =							pa.concat_tables([table for _, table in generator]	)
			assert pa_table.schema.field("label"	).type == ClassLabel(names=["good", "bad"]	)()
			UpperCamelCase__       =							pa_table.to_pydict()["label"]
			assert generated_content == [ClassLabel(names=["good", "bad"]	).straint(_A	) for label in labels]







def 							UpperCamelCase_(					_A :List[Any]	)->		Any:
			UpperCamelCase__       =							Csv(encoding="utf-8"					,  sep=","					,  converters={"int_list": lambda _A	: [int(_A	) for i in x.split()]}	)
			UpperCamelCase__       =							csv._generate_tables([[csv_file_with_int_list]]	)
			UpperCamelCase__       =							pa.concat_tables([table for _, table in generator]	)
			assert pa.types.is_list(pa_table.schema.field("int_list"	).type	)
			UpperCamelCase__       =							pa_table.to_pydict()["int_list"]
			assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


